Study on the spatial correlation effects and influencing factors of carbon emissions from the electricity industry: a fresh evidence from China

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of -797.74 Mt, -619.99 Mt, and -47.76 Mt, respectively.

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer

BACKGROUND AND OBJECTIVE

In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME.

MATERIALS AND METHODS

In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS).

RESULTS

The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001).

CONCLUSION

This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.

Spatiotemporal evolution and characteristics of worldwide life expectancy

Exploring global differences in life expectancy can facilitate the development of strategies to narrow regional disparities. However, few researchers have systematically examined patterns in the evolution of worldwide life expectancy over a long time period. Spatial differences among 181 countries in 4 types of worldwide life expectancy patterns from 1990 to 2019 were investigated via geographic information system (GIS) analysis. The aggregation characteristics of the spatiotemporal evolution of life expectancy were revealed by local indicators of spatial association. The analysis employed spatiotemporal sequence-based kernel density estimation and explored the differences in life expectancy among regions with the Theil index. We found that the global life expectancy progress rate shows upward then downward patterns over the last 30 years. Female have higher rates of spatiotemporal progression in life expectancy than male, with less internal variation and a wider spatial aggregation. The global spatial and temporal autocorrelation of life expectancy shows a weakening trend. The difference in life expectancy between male and female is reflected in both intrinsic causes of biological differences and extrinsic causes such as environment and lifestyle habits. Investment in education pulls apart differences in life expectancy over long time series. These results provide scientific guidelines for obtaining the highest possible level of health in countries around the world.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration

In this paper, we empirically study the spatial association network of PM_2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM_2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM_2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM_2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM_2.5. The greater the urbanization differences, the more likely the generation of PM_2.5 links is, while the opposite is true for differences in geographical distance.

Spatial shifting of COVID-19 clusters and disease association with environmental parameters in India: A time series analysis

BACKGROUND

The viability and virulence of COVID-19 are complex in nature. Although the relationship between environmental parameters and COVID-19 is well studied across the globe, in India, such studies are limited. This research aims to explore long-term exposure to weather conditions and the role of air pollution on the infection spread and mortality due to COVID-19 in India.

METHOD

District-level COVID-19 data from April 26, 2020 to July 10, 2021 was used for the study. Environmental determinants such as land surface temperature, relative humidity (RH), Sulphur dioxide (SO₂), Nitrogen dioxide (NO₂), Ozone (O₃), and Aerosol Optical Depth (AOD) were considered for analysis. The bivariate spatial association was used to explore the spatial relationship between Case Fatality Rate (CFR) and these environmental factors. Further, the Bayesian multivariate linear regression model was applied to observe the association between environmental factors and the CFR of COVID-19.

RESULTS

Spatial shifting of COVID-19 cases from Western to Southern and then Eastern parts of India were well observed. The infection rate was highly concentrated in most of the Western and Southern regions of India, while the CFR shows more concentration in Northern India along with Maharashtra. Four main spatial clusters of infection were recognized during the study period. The time-series analysis indicates significantly more CFR with higher AOD, O₃, and NO₂ in India.

CONCLUSIONS

COVID-19 is highly associated with environmental parameters and air pollution in India. The study provides evidence to warrant consideration of environmental parameters in health models to mediate potential solutions. Cleaner air is a must to mitigate COVID-19.

Analysing the global and local spatial associations of medical resources across Wuhan city using POI data

BACKGROUND

There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents' quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources.

METHODS

Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city.

RESULTS

(1) Pharmacies, clinics and community hospitals show "multicentre + multicircle", "centre + axis + dot" and "banded" distribution characteristics, respectively, but specialized hospitals and general hospitals present "single core" and "double core" modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive.

CONCLUSIONS

Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.

Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China

Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.

Spatially varying effects of measured confounding variables on disease risk

BACKGROUND

The presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence.

METHODS

We proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina.

RESULTS

The analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender.

CONCLUSION

The application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.

Early is left and up: Saccadic responses reveal horizontal and vertical spatial associations of serial order in working memory

Maintaining serial order in working memory is crucial for cognition. Recent theories propose that serial information is achieved by positional coding of items on a spatial frame of reference. In line with this, an early-left and late-right spatial-positional association of response code (SPoARC) effect has been established. Various theoretical accounts have been put forward to explain the SPoARC effect (the mental whiteboard hypothesis, conceptual metaphor theory, polarity correspondence, or the indirect spatial-numerical association effect). Crucially, while all these accounts predict a left-to-right orientation of the SPoARC effect, they make different predictions regarding the direction of a possible vertical SPoARC effect. In this study, we therefore investigated SPoARC effects along the horizontal and vertical spatial dimension by means of saccadic responses. We replicated the left-to-right horizontal SPoARC effect and established for the first time an up-to-down vertical SPoARC effect. The direction of the vertical SPoARC effect was in contrast to that predicted by metaphor theory, polarity correspondence, or by the indirect spatial-numerical association effect. Rather, our results support the mental whiteboard-hypothesis, according to which positions can be flexibly coded on an internal space depending on the task demands. We also found that the strengths of the horizontal and vertical SPoARC effects were correlated, showing that some people are more prone than others to use spatial references for position coding. Our results therefore suggest that context templates used for position marking are not necessarily spatial in nature but depend on individual strategy preferences.

Spatial patterns and associations of tree species at different developmental stages in a montane secondary temperate forest of northeastern China

Background

Secondary forests have become the major forest type worldwide. Research on spatial patterns and associations of tree species at different developmental stages may be informative in understanding the structure and dynamic processes of secondary forests.

Methods

In this study, we used point pattern analysis to analyze the spatial patterns and associations of tree species at seedling, sapling and adult stages in a 4ha plot in the montane secondary temperate forest of northeastern China.

Results

We found that species showed similar patterns at seedling, sapling and adult stages, and aggregation was the dominant pattern. The spatial patterns of tree species were mainly affected by habitat heterogeneity. In addition, the strength of positive or negative associated pattern among tree species would decrease with developmental stages, which attributed to neighborhood competition and plant size increasing.

Conclusions

Our results indicated that the spatial patterns and associations of tree species at seedling and sapling stages partly reflected that at adult stage; habitat heterogeneity and neighborhood competition jointly contributed to species coexistence in this secondary forest.

Spatial association between outdoor air pollution and lung cancer incidence in China

Background

Lung cancer is the most common cancer in China. Previous studies have indicated that lung cancer incidence exhibits remarkable spatial heterogeneity, and lung cancer is related to outdoor air pollution. However, the non-linear spatial association between outdoor air pollution and lung cancer incidence in China remains unclear.

Methods

In this study, the relationships between the lung cancer incidence of males and females from 207 counties in China in 2013 with annual concentrations of PM2.5, PM10, SO₂, NO₂, CO and O₃ were analysed. GeoDetector q statistic was used for examining the non-linear spatial association between outdoor air pollution and incidence of lung cancer.

Results

An apparent spatial and population gender heterogeneity was found in the spatial association between outdoor air pollution and lung cancer incidence. Among the six selected pollutants, SO₂ has the greatest influence on lung cancer (q = 0.154 in females) in north China. In the south, each selected pollutant has a significant impact on males or females, and the mean q value in the south is 0.181, which is bigger than that in the north (q = 0.154). In addition, the pollutants have evident non-linear interaction effects on lung cancer. In north China, the interaction between SO₂ and PM2.5 is the dominant interaction, with q values of 0.207 in males and 0.334 in females. In the south, the dominant interactive factors are between SO₂ and O₃ in males and between SO₂ and CO in females, with q values of 0.45, 0.232 respectively. Smoking is a substantial contributor to lung cancer among men, either in South or North China, with q value of 0.143 and 0.129 respectively, and the interaction between smoking and air pollutants increases this risk.

Conclusions

This study implies that the influence of SO₂ and PM2.5 on lung cancer should be focused on in north China, and in the south, the impact of O₃ and CO as well as their interaction with SO₂ need to be paid more attention. Smoking, particularly in men, remains a significant risk factor for lung cancer in both North and South China.

Influences of anthropogenic activities and topography on water quality in the highly regulated Huai River basin, China

Our study analyzed the spatio-temporal trends of four major water quality parameters (i.e., dissolved oxygen (DO), ammonium nitrogen (NH₃-N), total phosphorus (TP) and permanganate index (COD_Mn)) at 17 monitoring stations in one of the most polluted large river basins, Huai River Basin, in China during 2005 to 2014. More concerns were emphasized on the attributions, e.g., anthropogenic actives (land cover, pollution load, water temperature, and regulated flow) and natural factors (topography) to the changes in the water quality. The seasonal Mann-Kendall test indicated that water quality conditions were significantly improved during the study period. The results given by the Moran's I methods demonstrated that NH₃-N and COD_Mn existed a weak and moderate positive spatial autocorrelation. Two cluster centers of significant high concentrations can be detected for DO and TP at the Mengcheng and Huaidian station, respectively, while four cluster centers of significant low concentrations for DO at Wangjiaba and Huaidian station in the 2010s. Multiple linear regression analysis suggested that water temperature, regulated flow, and load of water quality could significantly influence the water quality variations. Additionally, urban land cover was the primary predictor for NH₃-N and COD_Mn at large scale. The predictive ability of regression models for NH₃-N and COD_Mn declined as the scale decreases or the period ranges from the 2000s to the 2010s. Topography variables of elevation and slope, which can be treated as the important explanatory variables, exhibited positive and negative correlations to NH₃-N and COD_Mn, respectively. This research can help us identify the water quality variations from the scale-process interactions and provide a scientific basis for comprehensive water quality management and decision making in the Huai River Basin and also other river basins over the world.

Modelling spatial association in pattern based land use simulation models

Pattern based land use models are widely used to forecast land use change. These models predict land use change using driving variables observed on the studied landscape. Many of these models have a limited capacity to account for interactions between neighbouring land parcels. Some modellers have used common spatial statistical measures to incorporate neighbour effects. However, these approaches were developed for continuous variables, while land use classifications are categorical. Neighbour interactions are also endogenous, changing as the land use patterns change. In this study we describe a single variable measure that captures aspects of neighbour interactions as reflected in the land use pattern. We use a stepwise updating process to demonstrate how dynamic updating of our measure impacts on model forecasts. We illustrate these results using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) system to forecast land use change for the Deep Creek watershed in the northern Okanagan Valley of British Columbia, Canada. Results establish that our measure improves model calibration and that ignoring changing spatial influences biases land use change forecasts.

Nonparametric spatial models for clustered ordered periodontal data

Clinical attachment level (CAL) is regarded as the most popular measure to assess periodontal disease (PD). These probed tooth-site level measures are usually rounded and recorded as whole numbers (in mm) producing clustered (site measures within a mouth) error-prone ordinal responses representing some ordering of the underlying PD progression. In addition, it is hypothesized that PD progression can be spatially-referenced, i.e., proximal tooth-sites share similar PD status in comparison to sites that are distantly located. In this paper, we develop a Bayesian multivariate probit framework for these ordinal responses where the cut-point parameters linking the observed ordinal CAL levels to the latent underlying disease process can be fixed in advance. The latent spatial association characterizing conditional independence under Gaussian graphs is introduced via a nonparametric Bayesian approach motivated by the probit stick-breaking process, where the components of the stick-breaking weights follows a multivariate Gaussian density with the precision matrix distributed as G-Wishart. This yields a computationally simple, yet robust and flexible framework to capture the latent disease status leading to a natural clustering of tooth-sites and subjects with similar PD status (beyond spatial clustering), and improved parameter estimation through sharing of information. Both simulation studies and application to a motivating PD dataset reveal the advantages of considering this flexible nonparametric ordinal framework over other alternatives.

Social structure of collared peccaries (Pecari tajacu): does relatedness matter?

Relatedness is considered an important factor in shaping social structure as the association among kin might facilitate cooperation via inclusive fitness benefits. We addressed here the influence of relatedness on the social structure of a Neotropical ungulate, the collared peccary (Pecari tajacu). As peccaries are highly social and cooperative, live in stable cohesive herds and show certain degree of female philopatry and high mean relatedness within herds, we hypothesized that kin would be spatially closer and display more amicable and less agonistic interactions than non-kin. We recorded spatial association patterns and rates of interactions of two captive groups. Pairwise relatedness was calculated based on microsatellite data. As predicted, we found that kin were spatially closer than non-kin, which suggests that relatedness is a good predictor of spatial association in peccaries. However, relatedness did not predict the rates of social interactions. Although our results indirectly indicate some role of sex, age and familiarity, further studies are needed to clarify the factors that shape the rates of interactions in collared peccaries. This article is part of a Special Issue entitled: Neotropical Behaviour.